LFM2.5-2.6B is a new tiny model by LiquidAI, with benchmarks that put it head to head with much larger models.

I've run llama-perplexity on many model GGUF quants, crossed with many KV cache quants, to understand the model's best overall quantization for any given amount of memory.

I also show how different quantization metrics show (or hide) model degradation.

Full report and commentary

Interactive HTML plots

If you don't have time to read

• The model fits on an 8GB Raspberry Pi with no material degradation and on a 4GB Raspberry Pi with contained degradation.
• DO NOT use Q4_K_M.
• On this model, model quant quality degrades faster than KV cache quant.
• Abliteration comes with a flat cost of ~0.075 KLD.
• Logarithmic KLD and Top-1% plots lie to you by telling you that quality degradation is smooth, while it's actually a cliff.